

'''simple docstring'''
import os
import sys
import unittest


_lowerCamelCase      					=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase      					=     os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase      					=     os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class 					_snake_case		(unittest.TestCase):






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  get_test_to_tester_mapping(SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      List[Any]						=  get_test_to_tester_mapping(SCREAMING_SNAKE_CASE_     )

				UpperCAmelCase_     :      Optional[Any]						=  {"BertModelTest": "BertModelTester"}

				UpperCAmelCase_     :      Union[str, Any]						=  {
				    "BlipModelTest": "BlipModelTester",
				    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
				    "BlipTextModelTest": "BlipTextModelTester",
				    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
				    "BlipVQAModelTest": "BlipVQAModelTester",
				    "BlipVisionModelTest": "BlipVisionModelTester",
				}

				self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_     )			,SCREAMING_SNAKE_CASE_     )
				self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_     )			,SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  get_model_to_test_mapping(SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      Any						=  get_model_to_test_mapping(SCREAMING_SNAKE_CASE_     )

				UpperCAmelCase_     :      Optional[Any]						=  {
				    "BertForMaskedLM": ["BertModelTest"],
				    "BertForMultipleChoice": ["BertModelTest"],
				    "BertForNextSentencePrediction": ["BertModelTest"],
				    "BertForPreTraining": ["BertModelTest"],
				    "BertForQuestionAnswering": ["BertModelTest"],
				    "BertForSequenceClassification": ["BertModelTest"],
				    "BertForTokenClassification": ["BertModelTest"],
				    "BertLMHeadModel": ["BertModelTest"],
				    "BertModel": ["BertModelTest"],
				}

				UpperCAmelCase_     :      List[Any]						=  {
				    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
				    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
				    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
				    "BlipModel": ["BlipModelTest"],
				    "BlipTextModel": ["BlipTextModelTest"],
				    "BlipVisionModel": ["BlipVisionModelTest"],
				}

				self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_     )			,SCREAMING_SNAKE_CASE_     )
				self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_     )			,SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  get_model_to_tester_mapping(SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      Any						=  get_model_to_tester_mapping(SCREAMING_SNAKE_CASE_     )

				UpperCAmelCase_     :      Optional[Any]						=  {
				    "BertForMaskedLM": ["BertModelTester"],
				    "BertForMultipleChoice": ["BertModelTester"],
				    "BertForNextSentencePrediction": ["BertModelTester"],
				    "BertForPreTraining": ["BertModelTester"],
				    "BertForQuestionAnswering": ["BertModelTester"],
				    "BertForSequenceClassification": ["BertModelTester"],
				    "BertForTokenClassification": ["BertModelTester"],
				    "BertLMHeadModel": ["BertModelTester"],
				    "BertModel": ["BertModelTester"],
				}

				UpperCAmelCase_     :      int						=  {
				    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
				    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
				    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
				    "BlipModel": ["BlipModelTester"],
				    "BlipTextModel": ["BlipTextModelTester"],
				    "BlipVisionModel": ["BlipVisionModelTester"],
				}

				self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_     )			,SCREAMING_SNAKE_CASE_     )
				self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_     )			,SCREAMING_SNAKE_CASE_     )






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


